Image segmentation is often used to identify regions of interest for use in medical image analysis. In particular, image segmentation is used to segment structures from the background and is often used as a first step for medical image analysis, such as for visualization, quantitative image analysis, and image guided intervention.
Image segmentation can be difficult to perform because of the large variability of shape and appearance of different structures, including the lack of contrast between adjacent or neighboring structures. Known image segmentation methods are generally divided into local image-based approaches and atlas-based approaches. For example, image-based approaches segment based on image cues including intensity, gradient, and/or texture. Image based methods use different models that perform generally well when structures of interest have prominent boundaries and the intensities of neighboring structures are different. However, these methods often perform poorly when these conditions are not met. While prior anatomical knowledge might help alleviate such limitations it is difficult to incorporate this information into image-based approaches especially information about multi-structure segmentation.
Atlas-based approaches rely largely on prior knowledge about the spatial arrangement of structures. These approaches typically include first registering one or more of the images (atlases) to the subject image target, so that the manual segmentations from the atlas(es) can be propagated and fused. Compared to image-based approaches, these methods incorporate anatomical knowledge for improved performance, but are limited by large anatomical variation and imperfect registration.
Multi-atlas based methods have been a trend for robust and automated image segmentation. In general, these methods first transfer prior manual segmentations, i.e. label maps, on a set of atlases to a given target image through image registration. The multiple label maps are then fused together to produce segmentations of the target image, by way of two utilized fusion strategies through voting strategy or statistical fusion, e.g. Simultaneous Truth and Performance Level Estimation (“STAPLE”), an algorithm for the validation of image segmentation. Different from most voting-based methods, STAPLE does not assume the atlases perform equally well on the target image. Instead, the atlas labeling performance levels for the structures of interest are modeled and incorporated into a probabilistic framework which is solved for the true segmentation. STAPLE simultaneously estimates the true segmentation and the label map performance level, but has been shown inaccurate for multi-atlas segmentation because it is determined on propagated label maps and not on the target image intensity. This makes STAPLE more robust to anatomical variation between the atlas images and the target image, advantageous over majority voting. STAPLE (as well as voting strategy), however, blindly fuses the labels without considering target image intensity information, permitting errors especially at the region boundaries.
In further explanation, STAPLE fuses labels based on the propagated atlas labels without considering the target image. Therefore, when the target image exhibits large anatomical variation from the atlas images, the registration step may consistently fail on certain structures and STAPLE will not work. In addition, STAPLE is less accurate along structure boundaries.
Weighted fusion methods have also been proposed to improve performance where the segmentation fusion is weighted based on the intensity similarity between the target and the atlas images. However, information about structure intensity and contour that is specific to the subject's anatomy is not used, which makes it difficult to apply these methods to subjects with large anatomical differences from the atlases. Other methods have also been proposed and include an adaptive atlas method that allows large structure variation based on target image intensities. However, adaptive atlas methods do not consider structure boundary information, which means these methods cannot discriminate different structures that have similar intensities. Still other proposed methods use spectral label fusion that divides the target image into regions based on image intensities and contours, followed by voting on the regions using an atlas-based approach. These methods, however, are usually limited to a single anatomical region and would be difficult to extend to segment multiple regions simultaneously.
Thus, known segmentation methods suffer from different drawbacks as a result of using such image-based approaches or atlas-based approaches. Characterizing the performance of image segmentation poses an ongoing challenge, especially given the limited accuracy and precision during segmentation. Furthermore, interactive drawing of desired segmentation by human raters and performance by algorithmic raters creates unknown variability, performance of which is difficult to quantify because of the difficulty in obtaining or estimating a known true segmentation for clinical data. The following sets forth a new method and system that addresses these deficiencies.